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the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning modelling. This exciting project involves the application of innovative
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complex, high dimensional and high-volume datasets. Uses data preparation, modeling and predictive modeling, analysis, processing, algorithms, and systems. Applies knowledge of statistics, machine learning
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project working to develop real-time vector-borne disease risk assessment in low resource areas. The individual will be directly responsible for the development of adaptive predictive models for nowcasting
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prediction, focusing on efficient edge deployment (e.g., through model pruning, quantization, or TinyML techniques). The embedded system will be designed to perform local inference in real-time, minimizing
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their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in the field of genomics, evolution, population
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methodologies. The focus is on across-organ imaging, ranging from non-human primate (NHP) models to human applications. You will contribute to the development and application of state-of-the-art MRI techniques
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skills are required for the job: - Computational modeling of molecular crystals. - Computational and theoretical chemistry. - Crystal structure prediction. - Familiarity with the Linux operating system
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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the collection of empirical data through field trials and the development of prediction models based on these data. The candidate is to perform a variety of functions related to research. The candidate is expected
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the collection of empirical data through field trials and the development of prediction models based on these data. The candidate is to perform a variety of functions related to research. The candidate is expected